Systems and methods for removing defects from images

ABSTRACT

A method is described to increase the efficiency of the removal of defects from document images by reorienting the conceptual framework within which an image is filtered. Rather than arbitrarily applying a filter to an entire landscape of a document image, the disclosure describes a methodology by which a document image is separated into regions of darkness and regions of light, or viewed alternatively, regions of darkness and regions of lack of darkness. Filtering is then adaptively applied to each region to remove defects.

AMENDED APPLICATION

In response to the Notice to File Corrected application Papers mailedJul. 15, 2013, Applicants respectfully submits the corrected applicationin its entirety. Applicants respectfully submit that no new matter hasbeen added.

FIELD OF THE INVENTION

The present invention relates to image processing, and moreparticularly, to computer methods and systems for removing defects fromdocument images.

BACKGROUND OF THE INVENTION

The widespread adoption of multifunctional mobile communications devicesby individuals and enterprises has made it possible to move manybusiness functions into the mobile realm. For instance, banks provideusers with mobile phone applications that allow the users to depositchecks by photographing the check and then sending the image to the bankthrough a wireless mobile communications interface. It would beworthwhile if users could use mobile technology to transfer other typesof business documents as well. Unfortunately, this has proven difficultbecause images taken from mobile devices are not always of sufficientquality to allow recipients to extract data accurately therefrom.

Photographs taken from mobile devices typically have defects introducedby factors such as lighting, shadow, and the ability of the user to takea clean photo. Without correction, these defects negatively affect theaccuracy of data extraction. Therefore, prior to performing dataextraction, recipients of documents must remove defects. In the case ofchecks, this is not overly burdensome because checks are small and havea limited amount of information fields from which data requiresextraction (e.g. name, account number, routing number, etc.) However,larger documents create problems of scale. Larger documents have moredata to extract and correspondingly more defects to remove. Removing alarge number of defects greatly increases the time and processing powerneeded to extract information from larger documents. This has made itimpractical to use mobile devices as an efficient means of capturingdocuments for later information extraction.

BRIEF DESCRIPTION OF THE DRAWINGS

So that those having ordinary skill in the art, to which the presentinvention pertains, will more readily understand how to employ the novelsystem and methods of the present invention, certain illustratedembodiments thereof will be described in detail herein-below withreference to the drawings, wherein:

FIG. 1 is a system diagram for extracting and utilizing data from animage;

FIG. 2 is a flowchart that illustrates a process of extracting andutilizing data from an image;

FIG. 3 is a flowchart that illustrates removal of a defect from animage;

FIGS. 4A and 4B illustrate preprocessing, including the extraction anddewarping of a document image;

FIGS. 5A and 5H illustrate the division of the extracted and dewarpedimage of FIG. 4 into bit planes;

FIGS. 6A and 6B illustrate a target bit plane both before and afterapplication of a Gaussian blur and

FIGS. 7A and 7C illustrate images of FIGS. 4A and 4B both before andafter the application of the method of FIG. 3 and the comparison thereofto a conventional defect removal technique.

A component or a feature that is common to more than one drawing isindicated with the same reference number in each of the drawings.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

The present disclosure is directed to systems and methods for extractingand utilizing data from an image, including removal of defects from theimage, in the below illustrated embodiments. It is to be appreciated thesubject invention is described below more fully with reference to theaccompanying drawings, in which an illustrated embodiment of the presentinvention is shown. The present invention is not limited in any way tothe illustrated embodiment as the illustrated embodiment described belowis merely exemplary of the invention, which can be embodied in variousforms, as appreciated by one skilled in the art. Therefore, it is to beunderstood that any structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a basis for theclaims and as a representative for teaching one skilled in the art tovariously employ the present invention. Furthermore, the terms andphrases used herein are not intended to be limiting but rather toprovide an understandable description of the invention.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present invention, exemplarymethods and materials are now described. All publications mentionedherein are incorporated herein by reference to disclose and describe themethods and/or materials in connection with which the publications arecited.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an,” and “the” include plural referents unless thecontext clearly dictates otherwise. Thus, for example, reference to “astimulus” includes a plurality of such stimuli and reference to “thesignal” includes reference to one or more signals and equivalentsthereof known to those skilled in the art, and so forth.

It is to be appreciated the embodiments of this invention as discussedbelow are preferably a software algorithm, program or code residing oncomputer useable medium having control logic for enabling execution on amachine having a computer processor. The machine typically includesmemory storage configured to provide output from execution of thecomputer algorithm or program. As used herein, the term “software” ismeant to be synonymous with any code or program that can be in aprocessor of a host computer, regardless of whether the implementationis in hardware, firmware or as a software computer product available ona disc, a memory storage device, or for download from a remote machine.The embodiments described herein include such software to implement theequations, relationships and algorithms described above. One skilled inthe art will appreciate further features and advantages of the inventionbased on the above-described embodiments. Accordingly, the invention isnot to be limited by what has been particularly shown and described,except as indicated by the appended claims. All publications andreferences cited herein are expressly incorporated herein by referencein their entirety.

The methods described herein increase the efficiency of the removal ofdefects from document images by reorienting the conceptual frameworkwithin which an image is filtered. Rather then arbitrarily applying afilter to an entire landscape of a document image, the Applicants havecreated a methodology by which a document image is separated intoregions of darkness and regions of light, or viewed alternatively,regions of darkness and regions of lack of darkness. Filtering is thenadaptively applied to each region to remove defects.

In one embodiment, a computer implemented method for adaptivebinarization to remove a defect from an image is provided. Image data isreceived. The image data is grouped in a plurality of components. In theplurality of components, at least a first data type and a second datatype are identified. The plurality of components are processed to removethe at least one defect, wherein components belonging to the first datatype are processed through employment of a first mode and componentsbelonging to the second data type are processed through employment of asecond mode.

In another embodiment, a computer implemented method to remove a defectfrom an image is provided. An image is received. The image is separatedinto a plurality of components. The components are analyzed to determinea target component, the target component being the image component thatprovides an optimal amount of information, relative to the imagecomponents, regarding the location of the defect in the image. Thetarget component is analyzed to determine a positional boundary for thedefect in the image. Binarization is performed on the image, wherein afirst threshold values is used for data within the positional boundaryand a second threshold value is used for data outside of the positionalboundary.

In another embodiment, a computer implemented method to remove a defectfrom an image is provided. An image is received. Bit plane slicing isperformed on the image to produce a plurality of bit planes. The bitplanes are compared to identify a target bit plane, the target bit planeproviding a positional boundary of the defect. Binarization is performedon the image to remove the defect.

In another embodiment, a computer implemented method to remove a defectfrom an image is provided. An image is received. The image is processedto separate the image into at least one defect region and at least onenon-defect region. The image is binarized using a first threshold valuewithin the defect region and a second threshold value within thenon-defect region.

In another embodiment, a computer implemented method to remove a defectfrom an image is provided. Image data is received. The image data isprocessed to separate the image data into a plurality of image datasubgroups. A subgroup identifying a defect in the image is selected. Thesubgroup identifying the defect is utilized to determine a position ofthe defect within the image data. The image data is binarized such thata first threshold value is used on data at the position and secondthreshold value is used elsewhere in the data.

Referring to FIG. 1, a hardware diagram depicting a system 100 in whichthe processes described herein can be executed is provided for exemplarypurposes. In one example, system 100 includes at least one instance ofan image capture device 102. Exemplary embodiments of image capturedevice 102 include but are not limited to multifunction “smart phone”104 including an image capture capability, such as a digital camera. Inanother example, the image capture device could be a stand alone digitalcamera 106. In another example, the image capture device 102 could be astandalone video camera (e.g. webcam) 108. In another example, an imagecapture device 102 could be connected (e.g. wireless or wired) to acomputing device, such as a tablet 110 or notebook 112 computer.

Referring further to FIG. 1, system 100 includes at least one instanceof data extraction device 114. Data extraction device 114 is a hardwareand/or software component residing on a server or computer. In anotherexample, data extraction device 114 is a hardware and/or softwarecomponent residing on multiple servers. In another example, the dataextraction device 114 is a hardware and/or software component residingon image capture device 102.

It should be understood that image capture devices 102 each generallyinclude at least one processor, at least one interface, and at least onememory device coupled via buses. Image capture devices 102 may becapable of being coupled together, coupled to peripheral devices, andinput/output devices. Image capture devices 102 are represented in thedrawings as standalone devices, but are not limited to such. Each can becoupled to other devices in a distributed processing environment.

It should further be noted that each image capture device 102 and dataextraction device 114 may include or be coupled to input devices, suchas keyboards, speech recognition systems, mouses, trackballs, joysticks,etc. It is also understood that image capture devices 102 and dataextraction device 114 may include or be coupled to output devices, suchas speakers, display devices and so forth to allow users to receiveoutput of the processes described herein. Further, image capture devices102 and data extraction device 114 may be connected to variousperipheral devices

Data extraction device 114 includes a defect removal engine 116, a datarecognition engine 117, memory 118, and a processor 119. Defect removalengine 116 comprises hardware and/or software components programmed toperform defect removal as further set forth herein. Data recognitionengine 117 in one example is an optical character recognition (“OCR”)engine or program that is utilized to perform optical characterrecognition on document images. In one embodiment, data recognitionengine 117 performs optical character recognition on document imagessubsequent to their being sent to defect removal engine 116 for removalof defects from document images.

Defect removal engine 116 and data recognition engine 117 containinstructions for controlling processor 119 to execute the methodsdescribed herein. Examples of these methods are explained in furtherdetail in the subsequent of exemplary embodiments section-below. Theterm “engine” is used herein to denote a functional operation that maybe embodied either as a stand-alone component or as an integratedconfiguration of a plurality of subordinate components. Thus, defectremoval engine 116 and data recognition engine 117 may be implemented asa single module or as a plurality of modules that operate in cooperationwith one another. Moreover, although defect removal engine 116 and datarecognition engine 117 are described herein as being implemented assoftware, they could be implemented in any of hardware (e.g. electroniccircuitry), firmware, software, or a combination thereof.

Memory 118 is a computer-readable medium encoded with a computerprogram. Memory 118 stores data and instructions that are readable andexecutable by processor 119 for controlling the operation of processor119. Memory 118 may be implemented in a random access memory (RAM),volatile or non-volatile memory, solid state storage devices, magneticdevices, a hard drive, a read only memory (ROM), or a combinationthereof.

Processor 119 is an electronic device configured of logic circuitry thatresponds to and executes instructions. The processor 119 could comprisemore than one distinct processing device, for example to handledifferent functions within data extraction device 114. Processor 119outputs results of an execution of the methods described herein.Alternatively, processor 119 could direct the output to a remote device(not shown) via network 120.

It is to be further appreciated that network 120 depicted in FIG. 1 caninclude a local area network (LAN) and a wide area network (WAN), butmay also include other networks such as a personal area network (PAN).Such networking environments are commonplace in offices, enterprise-widecomputer networks, intranets, and the Internet. For instance, when usedin a LAN networking environment, the system 100 is connected to the LANthrough a network interface or adapter (not shown). When used in a WANnetworking environment, the computing system environment typicallyincludes a modem or other means for establishing communications over theWAN, such as the Internet. The modem, which may be internal or external,may be connected to a system bus via a user input interface, or viaanother appropriate mechanism. In a networked environment, programmodules depicted relative to the system 100, or portions thereof, may bestored in a remote memory storage device such as storage medium. It isto be appreciated that the illustrated network connections of FIG. 1 areexemplary and other means of establishing a communications link betweenmultiple computers may be used.

In one example, image capture devices 102 will include a thin clientthat will allow the user to take pictures of documents and send thedocuments to data extraction device 114. Image capture devices 102 anddata extraction device 114 communicate over network 120 through one ormore communications interfaces 121. Communications interfaces 121 maycomprise either wired 122 or wireless 124 interfaces. Image capturedevices 102 send document images to data extraction device 114 throughone or more interfaces 121. It should be noted, however, that images canalso be sent directly or indirectly to the data extraction device 114.For instance, an image could be sent to a cloud or filing sharing sitefrom data extraction device 114 can retrieve the document image. Inanother example, the image could be provided to the data extractiondevice 114 through the use of storage media, such as a disc or a flashdrive.

Finally, referring further to FIG. 1, system 100 includes one or morecomputing devices 126 that use the data and images extracted by dataextraction device 114 for various business cases. An enterprise coulduse data extraction device 114 to extract data from documents and thenautomatically route images of the documents and data contained thereinto the proper party in the enterprise for further processing. Forexample, customers of an insurance company could use image capturedevices 102 to take images of documents, such as insurance declarationsheets and send the images to their insurance company. The insurancecompany could then use data extraction device 114 to extract certaininformation (e.g. name, coverage limits, policy number, etc.) from thedeclaration sheet. This information could then be used to automaticallyroute the image of the declaration sheet to the correct party or filewithin the insurance company for further processing.

It should be understood that computing devices 126 each generallyinclude at least one processor, at least one interface, and at least onememory device coupled via buses. Computing devices 126 may be capable ofbeing coupled together, coupled to peripheral devices, and input/outputdevices. Computing devices 126 are represented in the drawings asstandalone devices, but are not limited to such. Each can be coupled toother devices in a distributed processing environment.

Referring to FIG. 2, an exemplary flow diagram depicts a method 200 forextracting and utilizing data from an image. In step 202, an imagecapture device 102 captures an image. In step 204, the document image issent to a data extraction device 114 over one or more interfaces 122,124. In step 206, data extraction device 114 receives the image. Dataextraction device 114 passes the image to defect removal engine 116. Instep 208, defect removal engine 116 prepares the image for furtherprocessing. In one example, defect removal engine 116 prepares thedocument image for further processing by data recognition engine 117 byremoving defects from the image. In one example, the defects are shadowscontained in the image when the image capture device 102 captures theimage. In another example, the defects are the result of defects withinthe object of the image (e.g. smudged ink on a document).

The cleaned image (i.e. image with defects removed) is sent to datarecognition engine 118 in step 210. Data recognition engine 117 thenprocesses the cleaned image to extract data therein. In step 212 datarecognition engine 117 sends the image and/or extracted data tocomputing devices 126 for further processing to support the businesscase within the enterprise.

Referring to FIG. 3, an exemplary flow diagram depicts a method 300 bywhich defect removal engine 116 removes defects from an image. In step302, defect removal engine 116 preprocesses an image. Referring to FIGS.4A and 4B, in one example, the image comprises a document image 402. Dueto the limitations of image capture devices 102, document images takenfrom image capture devices do not appear flat (best shown in FIG. 4A).Further, such document images 402 sometimes include objects orbackground 404 in the periphery of image. FIGS. 4A and 4B depict apre-processing technique in which document image 402 is processed todewarp (i.e. make flat) and extract the image 402 from its background404. The result is designated as 406. In addition, if necessary,preprocessing includes converting the image 402 to a grayscale image. Inone example, the image 402 is converted to an 8 bit grayscale image.Furthermore, preprocessing may also include gray level plane extractionfrom a RGB color band wherein a given band is retrieved from image 402and that particular band is an 8 bit grayscale image

Referring now to FIG. 3, after preprocessing, the data comprisingpreprocessed image 406 is, in step 304, grouped or divided intosubgroups. In one example, these groupings or subgroupings are by formedby dividing the bit groupings for each pixel according to bit position.In one embodiment these groupings are formed through bit plane slicing.Referring to FIGS. 5A to 5H, image 406 is divided into 8 bit planes. BitPlane 0 (FIG. 5A) contains the most significant bit for each pixel inthe image. Bit plane 1 (FIG. 5B) contains the next most significant bitfor each pixel in the image 406. Bit plane 2 (FIG. 5C) contains nextmost significant bit for each pixel in the image 406. Bit plane 3 (FIG.5D) contains the next most significant bit, after bit plane 2, for eachpixel in the image 406. Bit plane 4 (FIG. 5E)_contains the nextsignificant bit, after bit plane 3, for each pixel in the image and soforth. Bit planes 1-3 yield binarized versions of the image 406, butwith black blotches 502 and white blotches 504. When compared to image406, blotches 502, 504 occur in areas where defects, in the form ofshadows, are present, as light transitions to dark and vice versa.Therefore, method 300 separates the image 406 into areas of light anddarkness or light and absence of light.

Referring now to FIG. 3, in step 306, a target bit plane is selectedfrom image 406. The target bit plane is selected by identifying the bitplane that contains the most actionable information regarding thelocation of defects in the image 406. For instance, it can be seen fromFIGS. 5A to 5H, that bit planes 0-2 (FIGS. 5A to 5C) provide littleinformation about the location of defects within image 406, because theyshow a minimal number of blotches whereas bit planes 4-7 (FIGS. 5E to5H) provide too much information because they comprise blotches that areinterspersed or collapsed together to the extent that are notrecognizable. Accordingly, bit plane 3 (FIG. 5D) is selected as thetarget bit plane 506. Put another way, bit planes 4-7 contain too muchbinary noise. Such that that binary noise would interfere with theextraction of defects from image 406. Accordingly, step 306 involvesselecting, as the target bit plane, the bit plane with an optimal amountof information regarding the positional boundaries of defects and aminimal amount of binary noise.

Referring further to FIG. 3, once target bit plane 506 is selected, afiltering operation is utilized, in step 308, to remove text from thetarget bit plane 506. Such filtering is worthwhile because edgedetection will be performed in step 310. Edge detection techniques aresusceptible to noise and text can be confused with blotches 502, 504when performing edge detection, and therefore provide inaccurate resultsas to the positional boundaries of blotches 502, 504.

Referring to FIGS. 6A and 6B, one exemplary filtering operation is thatmay be utilized in step 308 is edge boundary minimization. In oneexample, edge boundary minimization involves applying a Gaussian Blur tothe target bit plane 506. In another example, a Gaussian Blur is appliedto target bit plane 506 and the result is then thresholded to binarycolor constraints. The result is an optimized image 602 in which theboundaries 603 between blotches 502, 504 are clearly defined and text isremoved from the image.

Once optimized image 602 is created, an edge detection technique isperformed on it, in step 310, to identify the positional data regardingthe location or boundaries of blotches 502, 504 within optimized image603, i.e., the location in image 602 where the discontinuities betweenlight and shade occur. In one example, a technique utilizing adjacentsimilar color selection is utilized to detect the edges of blotches 502,504. Adjacent similar color selection selects a value for a pixel basedon the value of adjacent pixels around it. Accordingly, pixels that areon the border of blotches 502 that are assigned a dark value, but arepredominantly surrounded by pixels having a light value will be assignedas light. Conversely, pixels the border of blotches 504 that areassigned a light values, but are predominately surrounded by pixelshaving a dark value will be assigned a dark value. In this manner, theborders between blotches 502, 504 can be sharply defined.

Referring now to FIG. 3, in step 310, the position data from the edgedetection technique is used to adaptively filter image 406. In oneexample, adaptive histogram binarization is used on blotches 502, 504 toremove defects. Adaptive histogram binarization uses a threshold valueto determine whether a particular pixel should be designated light ordark. Pixels greater than the threshold value are designated light andpixels less than the threshold value are designated dark. In documentimage 406, text data 508 will have the darkest value due to textgenerally being darker than shadow. Therefore, within the positionalboundaries of the blotches 502, 504, histogram binarization is performedusing a lower threshold value than for the rest of the image.

Referring to FIGS. 7A to 7C, image 406 is shown next to image 702. Image702 (FIG. 7B) is a version of image 406 (FIG. 7A) after a conventionalnon-adaptive histogram binarization technique is used. Image 702 wasfiltered using a single threshold value for the entire image 406.Accordingly, image 702 contains a large defect 704 which obscures text.In contrast, image 706 (FIG. 7C) depicts image 406 after undergoing thedefect removal process described in FIG. 3. In contrast, image 704contains minimal defects and the text is not obscured.

The techniques described herein are exemplary, and should not beconstrued as implying any particular limitation on the presentdisclosure. It should be understood that various alternatives,combinations and modifications could be devised by those skilled in theart. For example, steps associated with the processes described hereincan be performed in any order, unless otherwise specified or dictated bythe steps themselves. The present disclosure is intended to embrace allsuch alternatives, modifications and variances that fall within thescope of the appended claims.

The terms “comprises” or “comprising” are to be interpreted asspecifying the presence of the stated features, integers, steps orcomponents, but not precluding the presence of one or more otherfeatures, integers, steps or components or groups thereof.

Although the systems and methods of the subject invention have beendescribed with respect to the embodiments disclosed above, those skilledin the art will readily appreciate that changes and modifications may bemade thereto without departing from the spirit and scope of the subjectinvention as defined by the appended claims.

What is claimed is:
 1. A computer implemented method for adaptivebinarization to remove a defect from an image, the method comprising:receiving image data; grouping the image data into a plurality ofcomponents, wherein each of the components comprises a plurality of bitgroupings; identifying, in the plurality of components, at least a firstdata type and a second data type; and processing the plurality ofcomponents to remove the at least one defect, wherein each bit group ofthe first data type is compared to a first threshold value and the bitgroup is set to a first color value if the number exceeds the firstthreshold value and the bit group is set to a second color value if thenumber does not exceed the first threshold value.
 2. The computerimplemented method of claim 1, wherein the step of processing theplurality of components comprises the step of performing imagebinarization on the plurality of image components.
 3. The computerimplemented method of claim 2, wherein the step of performing imagebinarization comprises selecting a first threshold value for the imagecomponents of the first type and selecting a second threshold value forthe image components of the second type.
 4. The computer implementedmethod of claim 3, wherein the first threshold value is greater than thesecond threshold value.
 5. The computer implemented method of claim 1,wherein the step of processing comprises comparing each bit group of thesecond data type to a second threshold value and setting the bit groupto the first color value if the number exceeds the second thresholdvalue and setting the bit group to the second color value if the numberdoes not exceed the second threshold value.
 6. The computer implementedmethod of claim 1, wherein the first data type represents an area ofrelative darkness in the image and the second data type represents anarea of relative lightness in the image.
 7. A device comprising: aprocessor; and a memory coupled with the processor, the memorycomprising executable instructions that when executed by the processorcause the processor to effectuate operations comprising: receiving imagedata; grouping the image data into a plurality of components, whereineach of the components comprises a plurality of bit groupings;identifying, in the plurality of components, at least a first data typeand a second data type; and processing the plurality of components toremove the at least one defect, wherein each bit group of the first datatype is compared to a first threshold value and the bit group is set toa first color value if the number exceeds the first threshold value andthe bit group is set to a second color value if the number does notexceed the first threshold value.
 8. The device of claim 7, wherein thestep of processing the plurality of components comprises a step ofperforming image binarization on the plurality of image components. 9.The device of claim 8, wherein the step of performing image binarizationcomprises selecting a first threshold value for the image components ofthe first type and selecting a second threshold value for the imagecomponents of the second type.
 10. The device of claim 9, wherein thefirst threshold value is greater than the second threshold value. 11.The device of claim 7, wherein the step of processing comprisescomparing each bit group of the second data type to a second thresholdvalue and setting the bit group to the first color value if the numberexceeds the second threshold value and setting the bit group to thesecond color value if the number does not exceed the second thresholdvalue.
 12. The device of claim 7, wherein the first data type representsan area of relative darkness in the image and the second data typerepresents an area of relative lightness in the image.
 13. A computerreadable storage medium that is not a signal comprising computerexecutable instructions that when executed by a computing device causesaid computing device to effectuate operations comprising: receivingimage data; grouping the image data into a plurality of components,wherein each of the components comprises a plurality of bit groupings;identifying, in the plurality of components, at least a first data typeand a second data type; and processing the plurality of components toremove the at least one defect, wherein each bit group of the first datatype is compared to a first threshold value and the bit group is set toa first color value if the number exceeds the first threshold value andthe bit group is set to a second color value if the number does notexceed the first threshold value.
 14. The computer readable storagemedium of claim 13, wherein the step of processing the plurality ofcomponents comprises a step of performing image binarization on theplurality of image components.
 15. The computer readable storage mediumof claim 14, wherein the step of performing image binarization comprisesselecting a first threshold value for the image components of the firsttype and selecting a second threshold value for the image components ofthe second type.
 16. The computer readable storage medium of claim 15,wherein the first threshold value is greater than the second thresholdvalue.
 17. The computer readable storage medium of claim 13, wherein thestep of processing comprises comparing each bit group of the second datatype to a second threshold value and setting the bit group to the firstcolor value if the number exceeds the second threshold value and settingthe bit group to the second color value if the number does not exceedthe second threshold value.
 18. The computer readable storage medium ofclaim 13, wherein the first data type represents an area of relativedarkness in the image and the second data type represents an area ofrelative lightness in the image.